Ens, J. and Lawrence, P. (1993) An Investigation of Methods for Determining Depth from Focus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 97-108.
https://doi.org/10.1109/34.192482

has been cited by the following article:

• TITLE: Depth Estimation from a Single Image Based on Cauchy Distribution Model

  AUTHORS: Ying Ming

  KEYWORDS: Depth Estimation, Depth From Defocus, Defocus Blur, Gaussian Gradient, Cauchy Distribution, Point Spread Function (PSF)

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.3, March 31, 2021

  ABSTRACT: Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those methodsare suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on aCauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method wasrobust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.